The gradients w.r.t. the other variables can be computed in the same
way. The trainable parameters are initialized randomly and the
beamforming matrices \(\lbrace \mathbf {P}_k^0, \mathbf {W}_k^0,\mathbf {\theta }_{U_k}^0, \mathbf {\theta }_{F}^0 \rbrace \)  are initialized based on
the conventional channel alignment method [1]}. The
training process of the deep-unfolding NN is shown in Algorithm
REF , where \(\mathcal {H} \triangleq \lbrace \mathbf {H}_k^1, \mathbf {H}_k^2,\ldots , \mathbf {H}_k^N \rbrace \)  and \(N\)  is
determined by simulations.
